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Seagrass systems are in decline, mainly due to anthropogenic pressures and ongoing climate change. Implementing seagrass protection and restoration measures requires accurate assessment of suitable habitats. Commonly, such assessments have been performed using single-algorithm habitat suitability models, nearly always based on low environmental resolution information and short-term species data series. Here we address eelgrass (Zoostera marina) meadows' large-scale decline (>80%) in Shandong province (Yellow Sea, China) by developing an ensemble habitat model (EHM) to inform eelgrass conservation and restoration strategies in the Swan Lake (SL). For this, we applied a weighted EHM derived from ten single-algorithm models including profile, regression, classification, and machine learning methods to generate a high-resolution habitat suitability map. The EHM was constructed based on the predictive performances of each model, by combining a series of present-absent eelgrass datasets from recent years coupled with oceanographic and sediment data. The model was cross-validated with independent historical datasets, and a final habitat suitability map for conservation and restoration was generated. Our EHM scheme outperformed all single models in terms of habitat suitability, scoring ∼0.95 for both true statistic skill (TSS) and area under the curve (AUC) performance criteria. Machine learning methods outperformed profile, regression and classification methods. Regarding model explanatory variables, overall, topographic characteristics such as depth (DEP) and seafloor slope (SSL) are the most significant factors determining the distribution of eelgrass. The EHM predicted that the overlapping area was almost 90% of the current eelgrass habitat. Using results from our EHM, a LOESS regression model for the relationship of the habitat suitability to both the biomass and density of Z. marina outperformed better than the classic Ordinary Least Squares regression model. The EHM is a promising tool for supporting eelgrass protection and restoration areas in temperate lagoons as data availability improves. [Display omitted] • An ensemble model with high-resolution scale for eelgrass conservation targets. • Classic and non-parametric techniques model relation of HSI and eelgrass density. • The predictive model was constructed and cross-validated with long-term datasets. • Approach can be extended and applied to other costal restoration projects. [ABSTRACT FROM AUTHOR]